We propose a novel use of data collected incidentally during Electronic Health Records (EHR) use to help define care teams and care processes. We will develop tools and methods to visualize care networks across acute care and ambulatory settings that utilize an advanced homegrown clinical computing system and a commercial ICU system. We have chosen the clinical domain of maternal-child care because we can monitor continuity of care within a 10-month time window and our clinicians use EHRs in their offices, on the maternity wards and in the neonatal intensive care units. An EHR contains highly granular data regarding a patient's care, status and outcomes. In addition, these records contain data about who has entered information into the record and who has viewed it. This metadata can be used to infer who makes up the team of providers caring for an individual over time and to identify the relationships among patients'care teams. However, the patient-centric views of this data that are most used by clinicians and administrators as well as the methods used for data transfer to health information exchanges often subjugate, ignore or discard information that allows characterization of care team structure. The lack of attention paid to this class of information is especially relevant given recent work in multiple domains that demonstrates the importance of an individual's social ties on health status. The tools we develop will be used to apply and create network analytic metrics to understand the relationship between care team assembly and resulting structure and health outcomes. The proposed work will be done in a large tertiary care obstetric/newborn care service served by a robust EHR spanning outpatient and inpatient care. The patient population includes both high and low risk obstetrics as well as the full range of newborn care up to and including intensive care. The relatively homogenous nature of this population results in a limited number of co-morbidities that could potentially confound the relationship between care team structure and health outcomes. This will allow us to define a series of important relevant health outcomes and to rigorously evaluate the relation between care team structure and these outcomes. The knowledge gained and the tools created in this body of work are expected to have myriad applications that will extend beyond the current study population. At a basic level they will promote a greater understanding of how EHR-based data can be used to examine the complex social networks in which patients exist. At a practical level they will provide clinical leaders with real-time methods for monitoring and improving the quality of care teams that assemble around patients, particularly at critical junctures such as during the root cause analysis of outbreaks of antibiotic-resistant nosocomial infections.